One of the challenges faced by many businesses involves determining accurate risk factors associated with individuals. For example, insurance providers may set rates for applicants based on their known or assumed risk factors. A conventional method for determining risk utilizes general personal information such as age, sex, marital status, driving record, etc. The general personal information, along with actuarial, statistical, or empirical data associated with the general population is then utilized to help categorize individuals and set appropriate insurance rates. However, the individual may exhibit certain behaviors and habits, or may intermittently engage in high-risk activities that are not reflected in the general personal information, and the actual risk factors may differ widely from person-to-person within a given actuarial category.
In certain situations, it may be desirable to know the actual identity of a person involved in a specific incident. However, the general personal information is often insufficient for determining the habits, behaviors, or the actual identity of an individual. Conventional methods used by insurance providers to determine costs of motor vehicle insurance involve gathering relevant personal data from the applicant and referencing the applicant's public motor vehicle driving records and historical accident data. Such data generally results in a classification of the applicant to a broad actuarial class for which insurance rates are assigned based upon empirical experiences of an insurance provider. Various factors can be relevant to classification in a particular actuarial class, such as age, sex, marital status, home location, and driving record. Based on the personal data received from and about the applicant, the insurance provider can assign the applicant to an actuarial class and then assign an insurance premium based on that actuarial class.
Because a selected insurance premium is dependent on the applicant's personal data, a change to that personal data can result in a different premium being charged if the change results in a different actuarial class for the applicant. For instance, if a first actuarial class includes drivers between the ages of 36 and 40, and a second actuarial class includes drivers between the ages of 41 and 45, then a change in the applicant's age from 38 to 39 may not result in a different actuarial class, but a gradual change from 38 to 45 may result in a changed actuarial class and thus a changed insurance premium.
A principal issue associated with these conventional insurance determination systems is that the personal data collected from the applicant is generally not verifiable. For instance, the insurance provider may have no means to verify the applicant's mileage per year or the applicant's driving styles, either of which can be relevant to the selected insurance premium. Accordingly, the insurance provider's categorization of the applicant into a certain actuarial class may be based on false or incomplete information about the applicant, which can in turn result in an insurance premium that does not accurately reflect the risk of insuring the applicant.